Hi all,
Hope you can help me with this one as I'm having a bit of trouble.
Currently, I'm doing an experiment which has RNA-Seq data on a lot of samples from a differentiation procedure under a few different conditions.
I'd be very interested in looking into what cell types each condition/time combination clustered into. So far, I have found Sincera (R package) to work pretty well. The results line up with our observed phenotype quite well, but not perfectly. It would be really great if we could generate another set of results with a different method/pipeline and examine those, too, as a comparison.
I have so far looked into AUCell and EWCE, another set of R packages, however, these are both very poorly documented (Or perhaps my reading comprehension is just falling down this late on a Friday) and I am not confident they can achieve the kind of "cell type set enrichment" that I want.
Does anybody know of any tools which are designed to do this in a simple way, such as with Sincera (Filter, normalise, analyse and get results without a fuss)? Or perhaps anyone has a lot of experience with EWCE or AUCell and tell me if I'm barking up the wrong tree by trying to apply them to non-scRNA-Seq data. The cell types I'm interested in are not within the brain, which seems to be what the best tools focus on.
Thanks a lot for your time, any attention/help very appreciated.
Can you specify whether you're working with bulk or single-cell RNA-seq? SINCERA was developed for scRNA-seq, but your title states "non-scRNA-seq".
Are you trying to do bulk RNA-seq data deconvolution? If yes, then you need smth like this - https://github.com/xuranw/MuSiC. Depending on your answer I might suggest few more, but I'm not sure if you just want to check what is the most similar cell type to the particular RNA-seq sample or if you want to get cell type composition of your RNA-seq sample.